Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0933-3657(97)00029-8
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dc.titleThe Colorectal Cancer Recurrence Support (CARES) System
dc.contributor.authorOng, L.S.
dc.contributor.authorShepherd, B.
dc.contributor.authorTong, L.C.
dc.contributor.authorSeow-Choen, F.
dc.contributor.authorHo, Y.H.
dc.contributor.authorTang, C.L.
dc.contributor.authorHo, Y.S.
dc.contributor.authorTan, K.
dc.date.accessioned2014-11-27T09:46:46Z
dc.date.available2014-11-27T09:46:46Z
dc.date.issued1997-11
dc.identifier.citationOng, L.S., Shepherd, B., Tong, L.C., Seow-Choen, F., Ho, Y.H., Tang, C.L., Ho, Y.S., Tan, K. (1997-11). The Colorectal Cancer Recurrence Support (CARES) System. Artificial Intelligence in Medicine 11 (3) : 175-188. ScholarBank@NUS Repository. https://doi.org/10.1016/S0933-3657(97)00029-8
dc.identifier.issn09333657
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/111302
dc.description.abstractColorectal cancer has risen in incidence to become the second commonest form of cancer in Singapore. The primary treatment is surgery but up to 50% of patients still suffer from recurrence of the cancer after surgery. Early identification of recurrence will increase the effectiveness of therapy and the survival of patients. This paper describes the CARES (Cancer Recurrence Support) System, whose objective is to predict the recurrence of colorectal cancer, using Case-based Reasoning (CBR), and supported by other techniques such as data mining and natural language processing. The CARES System employs CBR to compare and contrast between the new and past colorectal cancer patient cases, and makes inferences based on those comparisons to determine the high risk patient groups. The features and functionality of the system are described.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0933-3657(97)00029-8
dc.sourceScopus
dc.subjectCase-based reasoning
dc.subjectColorectal cancer prediction
dc.subjectRecurrence
dc.typeReview
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.contributor.departmentINSTITUTE OF SYSTEMS SCIENCE
dc.description.doi10.1016/S0933-3657(97)00029-8
dc.description.sourcetitleArtificial Intelligence in Medicine
dc.description.volume11
dc.description.issue3
dc.description.page175-188
dc.description.codenAIMEE
dc.identifier.isiutA1997YJ72800001
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